class_docstr: Adds stateful behavior and a numpy.ndarray-typed API to a closure with an
  expected return type Tuple[Tensor, Union[Tensor, Sequence[Optional[Tensor]]]].
class_docstr_tok: ["Adds","stateful","behavior","and","a","numpy.ndarray-typed","API","to","a","closure","with","an","expected","return","type","Tuple","[","Tensor",",","Union","[","Tensor",",","Sequence","[","Optional","[","Tensor","]","]","]","]","."]
code: def __call__(
      self, state: Optional[ndarray] = None, **kwargs: Any
  ) -> Tuple[ndarray, ndarray]:
      if state is not None:
          self.state = state
  
      try:
          value_tensor, grad_tensors = self.closure(**kwargs)
          value = self.as_array(value_tensor)
          grads = self._get_gradient_ndarray(fill_value=self.fill_value)
          index = 0
          for param, grad in zip(
              self.parameters.values(), grad_tensors
          ):
              size = param.numel()
              if grad is not None:
                  grads[index : index + size] = self.as_array(
                      grad.view(-1)
                  )
              index += size
      except RuntimeError as e:
          value, grads = _handle_numerical_errors(
              e, x=self.state, dtype=np_float64
          )
  
      return value, grads
  
file_path: botorch/botorch/optim/closures/core.py
imports: from __future__ import annotations
  from functools import partial
  from typing import Any, Callable, Dict, Optional, Sequence, Tuple
  import torch
  from botorch.optim.utils import _handle_numerical_errors, get_tensors_as_ndarray_1d, set_tensors_from_ndarray_1d
  from botorch.optim.utils.numpy_utils import as_ndarray
  from botorch.utils.context_managers import zero_grad_ctx
  from numpy import float64, full, ndarray, zeros
  from torch import Tensor
label_desc: Use image node_id 2 for calling the NdarrayOptimizationClosure obj's underlying member method code with example usage: obj.__call__(state) and returns: value,  grads